Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information. You have probably seen it on Sci-fi, and personal assistants like Siri, Cortana, and Google Assistant, and other virtual assistants that interact with through voice. These AI assistants in order to understand your voice they need to do speech recognition so as to understand what you have just said. Speech Recognition is a complex process, well I'm not going to teach you how to train a Machine Learning/Deep Learning Model to that, instead, I instruct you how to do that using google speech recognition API. As long as you have the basics of Python you can successfully complete this tutorial and build your own fully functioning speech recognition programs in Python.

"One of the things I get very concerned about is that, for so long, AI has been such a mystery. And in that blanket of mysteriousness, it's been built up as something magical. So much so that for the first number of years in this role, customers were coming to Cloud excited about AI as a technology, but not yet as a means to solve tactical business problems, as if any use of AI might be a magic wand," says Tracy Pizzo Frey, Senior Director, Outbound Product Management, Engagements & Responsible AI for Cloud AI & Industry Solutions at Google. The reality is, of course, very different. AI technology is not magic at all.

Online information systems currently heavily rely on the username and password traditional method for protecting information and controlling access. With the advancement in biometric technology and popularity of fields like AI and Machine Learning, biometric security is becoming increasingly popular because of the usability advantage. This paper reports how machine learning based face recognition can be integrated into a web-based system as a method of authentication to reap the benefits of improved usability. This paper includes a comparison of combinations of detection and classification algorithms with FaceNet for face recognition. The results show that a combination of MTCNN for detection, Facenet for generating embeddings, and LinearSVC for classification outperforms other combinations with a 95% accuracy. The resulting classifier is integrated into the web-based system and used for authenticating users.

Recently, significant advancements have been made in face recognition technologies using Deep Neural Networks. As a result, companies such as Microsoft, Amazon, and Naver offer highly accurate commercial face recognition web services for diverse applications to meet the end-user needs. Naturally, however, such technologies are threatened persistently, as virtually any individual can quickly implement impersonation attacks. In particular, these attacks can be a significant threat for authentication and identification services, which heavily rely on their underlying face recognition technologies' accuracy and robustness. Despite its gravity, the issue regarding deepfake abuse using commercial web APIs and their robustness has not yet been thoroughly investigated. This work provides a measurement study on the robustness of black-box commercial face recognition APIs against Deepfake Impersonation (DI) attacks using celebrity recognition APIs as an example case study. We use five deepfake datasets, two of which are created by us and planned to be released. More specifically, we measure attack performance based on two scenarios (targeted and non-targeted) and further analyze the differing system behaviors using fidelity, confidence, and similarity metrics. Accordingly, we demonstrate how vulnerable face recognition technologies from popular companies are to DI attack, achieving maximum success rates of 78.0% and 99.9% for targeted (i.e., precise match) and non-targeted (i.e., match with any celebrity) attacks, respectively. Moreover, we propose practical defense strategies to mitigate DI attacks, reducing the attack success rates to as low as 0% and 0.02% for targeted and non-targeted attacks, respectively.

Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information. You have probably seen it on Sci-fi, and personal assistants like Siri, Cortana, and Google Assistant, and other virtual assistants that interact with through voice. In order to understand your voice these virtual assistants need to do speech recognition. Speech Recognition is a complex process, so I'm not going to teach you how to train a Machine Learning/Deep Learning Model to do that. Instead, I will instruct you how to do it using google speech recognition API.

Machine learning (ML) can help you create innovative, compelling and unique experiences for your mobile users. Once you've mastered ML, you can use it to create a wide range of applications, including apps that automatically organize photos based on their subject matter, identify and track a person's face across a livestream, extract text from an image, and much more. If you want to enhance your Android apps with powerful machine learning capabilities, then where exactly do you start? In this article, I'll provide an overview of an SDK (Software Development Kit) that promises to put the power of ML at your fingertips, even if you have zero ML experience. By the end of this article, you'll have the foundation you need to start creating intelligent, ML-powered apps that are capable of labelling images, scanning barcodes, recognizing faces and famous landmarks, and performing many other powerful ML tasks.

Imagine if the things around your house could respond to your voice even when you were shouting over a smoke alarm, keep track of each individual wandering through the house, unlock your front door just by identifying your voice, and even identify your emotions. Those are all capabilities that Microsoft is preparing to add to its Project Oxford, a set of cloud-based machine learning services introduced last May at Microsoft's Build conference. Ars took a deep dive on Project Oxford's first wave of machine learning-based services last year. Those services performed a number of image processing and recognition tasks, offered text-to-speech and speech recognition services, and even converted natural language into intent-based commands for applications. The services are the same technology used in Microsoft's Cortana personal assistant and the Skype Translator service, which translates voice calls in six languages (and text messages in 50 languages) in real-time.

Detecting and reading text from photos has multiple use cases, be it clicking a picture of a printed text and automatically converting it into a digital file or the new age application of reading bills and invoices. Other interesting use cases include deep image search, understanding local business listing using street view images or when combined with text translation the ability to take a picture of a billboard in a foreign country and have it converted to your native language, the possibilities are limitless. Image text recognition is a class of computer vision problems which, among other things, includes OCR (optical character recognition) or text detection (used to find printed text on images) or handwritten text recognition. With the advancement of deep learning we have come a long way to get substantially better at text recognition, but still, the best companies in the business have much to cover before we can consider this problem as solved. Most of the major technology companies/cloud services provide APIs to recognize text in an image.

Industries around the world have caught AI fever. Developers around the world have made more ways than ever for the technology to automate, optimize and enable different services. Facebook, Alphabet, IBM, Microsoft, Amazon and other major companies are all working on AI projects, along with numerous tech startups. One such upstart, which leverages artificial intelligence and image recognition in part to enable commerce, is Clarifai. Founded in 2013, Clarifai utilizes neural networks and provides customers with an image and video recognition API.

Our API provides face recognition, facial detection, eye position, nose position, mouth position, and gender classification. If you have any questions ask! Just send an email to [email protected] Happy Hacking! -Stephen Face (Detection) – A computer vision api for facial recognition and facial detection that is a perfect face.com We currently have a free api for face detection. Animetrics Face Recognition – The Animetrics Face Recognition API can be used to detect human faces in pictures.